Distributed Document and Phrase Co-embeddings for Descriptive Clustering

Research output: Research - peer-reviewPaper

  • Authors:
  • Motoki Sato
  • Austin Brockmeier
  • Georgios Kontonatsios
  • Tingting Mu
  • John Goulermas
  • And 2 others
  • External authors:
  • Junichi Tsujii
  • Sophia Ananiadou


Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster. In this paper, we present a descriptive clustering approach that employs a distributed representation model, namely the paragraph vector model, to capture semantic similarities between documents and phrases. The proposed method uses a joint representation of phrases and documents (i.e., a co- embedding) to automatically select a descriptive phrase that best represents each document cluster. We evaluate our method by comparing its performance to an existing state-of-the-art descriptive clustering method that also uses co-embedding but relies on a bag-of-words representation. Results obtained on benchmark datasets demonstrate that the paragraph vector-based method obtains superior performance over the existing approach in both identifying clusters and assigning appropriate descriptive labels to them.

Bibliographical metadata

Original languageEnglish
Number of pages11
StateAccepted/In press - 2017
EventEuropean Chapter of the Association for Computational Linguistics - Valencia Conference Center, Valencia, Spain
Duration: 3 Apr 20177 Apr 2017
Conference number: 15


ConferenceEuropean Chapter of the Association for Computational Linguistics
Abbreviated titleEACL
Internet address